Thesis (Ph.D.)--Virginia Commonwealth University, 2010. / Prepared for: Dept. of Biostatistics. Title from title-page of electronic thesis. Bibliography: leaves 166-187.

Universiẗat, Diss., 2003--Tübingen.

Techn. University, Diss., 2003--Berlin.

Universiẗat, Diss., 2004--Jena.

Pseudomonas is the most common cause of chronic lung infections leading to death in cystic fibrosis patients. While chronic infection is extremely difficult to eradicate, the initial bacterial-host interactions prior to biofilm formation and establishment of chronic infections represents an attractive therapeutic target. It is clear that interaction between pathogens and the host is a very complex process and successful adaptation requires tight control of virulence factor expression. The aim of this project was to look for early changes in P. aeruginosa global gene expression in response to attachment to epithelial cells. P. aeruginosa PA01 was incubated with polarized HBE cells at a MOI of 100 for 4 hours and bacteria attached to epithelial cells (interacting) were collected separately from those in the supernatant (non-interacting). To minimize media effects observed by others, iron and phosphate were supplemented at appropriate levels to avoid expression changes due to limitation of these nutrients, as confirmed in our microarray experiments. Analysis of 3 independent experiments demonstrated that 766 genes were up or down regulated by more than 1.5 fold during attachment. Among these, 371 genes, including ion, oprC, as well as 3 genes in quorum-sensing systems and 9 genes involved in the pmrAB and phoPQ two-component regulatory systems were found to be induced in the interacting bacteria. On the other hand, 395 genes, including oprG outer membrane porin and pscP involved in type III secretion system were down regulated. To understand the roles of these differentially expressed genes, a cytotoxicity (LDH release) assay was performed and demonstrated that oprG and ion mutants were less capable than the wild type of killing HBE epithelial cells. These findings suggest that, under these interaction assay conditions, regulation of the expression of certain virulence factors provides a potential advantage for successful adaptation. In addition, a mutant lacking a filamentous hemagglutinin like protein was found to be less cytotoxic to HBE cells and also deficient in A549 epithelial cell binding, indicating that this probable non-pilin adhesin has multiple functions in P. aeruginosa. / Science, Faculty of / Microbiology and Immunology, Department of / Graduate

Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based on linear models for individual probe signals and using dynamic variable selection at the prediction level. The algorithms combine separate analyses based on the multiple probe sets to give a final confidence score for each candidate genotypes. Our proposed classification model achieved an accuracy level of >99.4% with 100% call rate for the SNP genotype data which is comparable with existing genotyping technologies. We discussed the appropriateness of the proposed model related to other existing high-throughput genotype calling algorithms. In this thesis we have explored three new ideas for classification with high dimensional data: (1) ensembles of various sets of predictors with built-in dynamic property; (2) robust classification at the prediction level; and (3) a proper confidence measure for dealing with failed predictor(s). We found that a mixture model for classification provides robustness against outlying values of the explanatory variables. Furthermore, the algorithm chooses among different sets of explanatory variables in a dynamic way, prediction by prediction. We analyzed several data sets, including real and simulated samples to illustrate these features. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any ‘bad data’ corresponding to image artifacts on the microarray slide or failure of a specific chemistry. Though motivated by this genotyping application, the proposed methodology would apply to other classification problems where the explanatory variables fall naturally into groups or outliers in the explanatory variables require variable selection at the prediction stage for robustness. / Science, Faculty of / Statistics, Department of / Graduate

Microarrays make it possible to monitor the expression of thousands of genes in parallel thus generating huge amounts of data. So far, several databases have been developed for managing and analyzing this kind of data but the current state of the art in this field is still early stage. In this paper, we comprehensively analyze the requirements for microarray data management. We consider the various kinds of data involved as well as data preparation, integration and analysis needs. The identified requirements are then used to comparatively evaluate eight existing microarray databases described in the literature. In addition to providing an overview of the current state of the art we identify problems that should be addressed in the future to obtain better solutions for managing and analyzing microarray data.

No description available.

No description available.

No description available.